1. Field
Certain aspects of the present disclosure generally relate to a wireless communication and, more particularly, to processing of a received wireless signal.
2. Background
Low-power body area networks (LP-BANs) represent a promising concept for healthcare applications such as continuous monitoring for diagnostic purposes, effects of medicines on chronic ailments, etc. The LP-BAN can consist of several acquisition circuits. Each acquisition circuit can comprise a wireless sensor that senses one or more vital signs and communicates them to an aggregator (i.e., an access terminal) such as a mobile handset, a wireless watch, or a Personal Data Assistant (PDA).
The raw sensor measurements and/or their processed versions can be of use for diagnostic purposes. These measurements can also be used to evaluate short and long term effectiveness of drugs and therapy. In such application, the sensors may need to be small, lightweight, have long battery life and low cost. This results into very low power requirements for sensing and communicating, as well as into low complexity processing at the sensors.
Signal communications between the sensors and the aggregator of the LP-BAN can be based on an Ultra-Wideband (UWB) approach because of high data rates that can be achieved within a relatively small distance between communicating nodes (i.e., the sensors and the aggregator). The UWB communications are radio communications that use a frequency bandwidth larger than 500 MHz. In comparison to narrow-band communications which rely on modulation of a carrier frequency, the large bandwidth of UWB communications allows sending signals with features well-localized in time. If a signal is more localized in time, then it is more spread in frequency. This allows communications based on pulses, while information can be encoded in a distance between pulses (i.e., a Pulse Position Modulation: PPM), in a pulse amplitude (i.e., a Pulse Amplitude Modulation: PAM) or in a pulse width (i.e., Pulse Width Modulation: PWM). One of the key advantages of pulse-based communication is ability to precisely localize time of arrival of the information (i.e., arrival of the pulse).
It is desirable that quantization of a received analog UWB pulse signal at the aggregator of the LP-BAN is implemented with a low cost and low power dissipation. In order to achieve these requirements, a number of quantization bits needs to be small. However, the small number of quantization bits can introduce distortion in the digitized UWB pulse signal, which negatively affects the reconstruction accuracy.
In addition, the received signal at the UWB device is typically based on a pulse signal corrupted by noise and by various channel effects. The pulse signal can be typically based on a symmetrical low-pass pulse. On the other hand, a parametric Finite Rate of Innovation (FRI) processing applied after the UWB receiver front-end requires an input signal based on the periodic-sinc pulse. Therefore, the received UWB pulse signal needs to be properly adjusted (i.e., equalized) before being processed by the FRI module.
However, the equalized pulse signal at the input of the FRI module can still be corrupted by a prohibitively high level of noise. The well-known Cadzow iterative algorithm can be used as an integral part of the FRI processing to de-noise the equalized signal. The standard Cadzow algorithm provides, given a Toeplitz Hermitian square matrix of dimension N×N associated with the noisy signal, a Toeplitz Hermitian square matrix of the same dimension with rank K, where K<<N. In order to achieve this low-rank approximation (i.e., signal de-noising), the standard Cadzow algorithm performs eigenvalue decomposition (EVD) of the Toeplitz Hermitian square matrix of dimension N×N, and reconstructs the “best” rank K approximation by keeping only the K principal eigenvalues and eigenvectors. The reconstructed rank-K matrix is made Toeplitz by averaging its diagonals. This process is iterated until convergence. However, the standard iterative Cadzow algorithm is slow and computationally complex.
A method is proposed in the present disclosure to quantize the received pulse signal by using a limited number of quantization bits, while information of the received pulse signal is preserved for accurate signal reconstruction. The quantized signal needs to be then equalized in such a way to generate an output signal based on the periodic-sinc signal suitable for the subsequent FRI processing. It is also proposed in the present disclosure to speed up the iterative denoising Cadzow algorithm applied to the noisy equalized signal as a part of the FRI processing.